This paper attempts to enhance the performance of a green energy induction motor drive. The electronic power converters become indispensable part of the renewable energy systems (RES). The solar photovoltaic (PV) system is efficiently operated with artificial neural network (ANN) based maximum power point tracking (MPPT) algorithm. The inverter topologies for the green drive scheme are analyzed. To improve the drive performance a reduced switch multilevel inverter (RSMLI) is employed. As indirect field oriented control (IFOC) is used, the drive demands on-line estimation of rotor resistance. A neural learning model reference adaptive scheme (NL-MRAS) based rotor resistance estimator is found to exhibit good dynamic performance. This work also investigates the performance of the green drive with an intelligent estimator. The performance enhancement of the green energy drive obtained by ANN based MPPT for the PV system, a reduced switch MLI and an intelligent estimator is presented. © 2015 Elsevier Ltd.

Chitra A

Department of Energy and Power Electronics

School of Electrical Engineering

Vellore Campus

Himavathi S.

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Renewable Energy

Multilevel inverters (MLI) are suitable for renewable energy sources as they operate at lower switching frequency and voltage. The sinusoidal PWM (SPWM) techniques are popularly employed in MLIs to improve the drive performance. However as the number of switches increase it leads to more switching losses, higher price and size of the inverter. In this study, a modified reduced switch MLI (RSMLI) with five switches is proposed. Various multilevel inverter topologies with different multicarrier SPWM techniques such as phase disposition, phase opposition disposition, and alternative phase opposition disposition (APOD) are derived, analysed and presented for the proposed RSMLI. All the inverter topologies are simulated and the scheme with lowest THD is identified. The hardware setup is built for the proposed RSMLI using F28335 digital signal processor (DSP) for pulse generation and the results are validated. The vector control scheme of industrial standard requires an accurate and fast Rr estimator for stable performance. Neural based Rr estimator proposed by the authors is implemented on FPGA. The proposed RSMLI fed induction motor drive is examined with and without the neural based Rr estimator. Encouraging results obtained using the proposed RSMLI are reported. © The Institution of Engineering and Technology 2015.

IET Power Electronics

Reduced switch multilevel inverter for performance enhancement of induction motor drive with intelligent rotor resistance estimator

Online estimation of rotor resistance is essential for high performance vector controlled drives. In this paper, a novel modified neural algorithm has been identified for the online estimation of rotor resistance. Neural based estimators are now receiving active consideration as they have a number of advantages over conventional techniques. The training algorithm of the neural network determines its learning speed, stability, weight convergence, accuracy of estimation, speed of tracking and ease of implementation. In this paper, the neural estimator has been studied with conventional and proposed learning algorithms. The sensitivity of the rotor resistance change has been tested for a wide range of variation from −50% to +50% on the stability of the drive system with and without estimator. It is quiet appealing to settle with optimal estimation time and error for the viable realization. The study is conducted extensively for estimation and tracking. The proposed learning algorithm is found to exhibit good estimation and tracking capabilities. Besides, it reduces computational complexity and, hence, more feasible for practical digital implementation. © 2014, Higher Education Press and Springer-Verlag Berlin Heidelberg.

Frontiers in Energy

A modified neural learning algorithm for online rotor resistance estimation in vector controlled induction motor drives

Induction motors are the workhorses of industries. The two viable speed control schemes for the induction motor are V/f and indirect field oriented control (IFOC) technique. The voltage source inverter is the conventional power converter employed in the drive scheme. Recently, in medium power applications the multilevel inverters (MLI) are gaining more attention due to their high performance. In this work an attempt is made to develop an MLI fed induction motor drive with V/f control as well as with IFOC technique. The closed loop drive scheme is built and simulated using Matlab / Simulink. The performance of the drive scheme with both the control techniques are exhaustively studied under various conditions and the results are presented. Simulation results of IFOC scheme are provided as it exhibits better dynamic performance compared with the V/f scheme. The performance parameters of the drive with both the control schemes are compared and tabulated. © 2013 IEEE.

2013 Annual IEEE India Conference (INDICON)

Performance analysis of MLI fed induction motor drive with IFOC speed control

This work envisages modeling solar photovoltaic (PV) systems as it is one of the most promising renewable energy source. A mathematical model of the PV system assumes importance as it enables the study of its performance under varying environmental and load conditions so as to assess its suitability for real time applications. To serve this purpose a mathematical model of the PV system has been developed in Matlab. The simulator is modeled to support a large array PV system, so that it can be interfaced with power electronic converters. Also the PV model has been connected with the cascade multilevel inverter (MLI) and the results are presented. The simulation results illustrate that this inverter topology is a right choice for PV applications. © 2013 IEEE.

Journal	Data powered by TypesetRenewable Energy
Publisher	Data powered by TypesetElsevier BV
ISSN	0960-1481
Open Access	No